Machine Learning Course
Spring, 1997/8, M.Sc. programme in AI,
Information Technology Department,
Faculty
of Mathematics and Informatics, Sofia
University, Lecturer: Zdravko Markov
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Introduction - the AI approach
to Machine Learning, subfields
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Inductive learning
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Introducing basic concepts by example
- learning semantic networks
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General setting for induction
- languages, orderings, generalization and specialization operators, structuring
the hypothesis space
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Languages
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Searching the hypothesis space
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Top-down relational learning
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Hypothesis evaluation
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True error estimation
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Bayes and Occam approaches
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Evaluation of propositional hypotheses
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Evaluation of relational hypotheses
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Computational learning theory
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Learning in the limit
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PAC-learning
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Memory-based (lazy) learning
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Unsupervised Learning
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Data Mining and Knowledge Discovery
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Explanation-based learning
Introduction
to Machine Learning
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Readings
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Markov, Z. Introduction to Machine Learning (slides),
1998
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Markov, Z. Inductive methods for Machine Learning, TEMPUS JEN 1497 -
SOFTEX, 1996 (in Bulgarian).
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Markov, Z. Lecture Notes in Inductive Learning,
1998 (in Bulgarian).
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Markov, Z. Lecture Notes in Unsupervised Learning,
1998 (in Bulgarian).
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Markov, Z. Machine Learning Course, 1992
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Pelov, N. Algebraic Approach to ML, M.S. Thesis,
Faculty of Mathematics & Informatics - University of Sofia, 1997.
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Bratko, I. Prolog Programming for Artificial Intelligence (second edition),
Addison-Wesley, 1990.
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Flach, P. Simply Logical: Intelligent reasoning by example, John Wiley
& Sons, 1994.
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Genesereth, M. and N. Nilsson. Logical Foundations of Artificial Intelligence,
Morgan Kaufmann, 1987.
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Lavrac, N. and S. Dzeroski. Inductive Logic Programming: Techniques
and Applications, Ellis Horwood (Simon & Schuster), 1994.
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Luger,G.F., W.A. Stubblefield. Artificial Intelligence: structures and
strategies for complex problem solving (Second edition). The Benjamin/Cummings
Publishing Company, Inc., 1993.
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Michalski, R. and R. Stepp. Learning from observation: conceptual clustering,
in: Michalski, Carbonell and Mitchell (eds.), Machine Learning: Artificial
Intelligence Approach, Vol.1, Tioga, 1983.
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Michalski, Carbonell and Mitchell (eds.), Machine Learning: Artificial
Intelligence Approach, Vol.1, Tioga, 1983.
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Michalski, R., J. Carbonell and T. Mitchell. Machine Learning - Artificial
Intelligence Approach (Volume 2). Mogran Kaufmann, 1986.
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Winston, P. Artificial Intelligence (third edition), Addison-Wesley,
1992.
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ML related information on WWW
Learning Semantic
Networks by Examples
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Readings
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Markov, Z. Inductive methods for Machine Learning, Ch. 2.
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Winston, P. Artificial Intelligence (third edition), Addison-Wesley,
1990.
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Bratko, I. Prolog Programming for Artificial Intelligence (second edition),
Addison-Wesley, 1990, Ch.18, Sections 3,4.
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Programs
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Data
General setting
for induction, languages, structuring the hypothesis space (partial orderings,
lgg's)
Transforming
relational induction into propositional
Version Space Learning
Decision trees
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Readings
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Markov, Z. Inductive methods for Machine Learning, Ch. 6.
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Programs
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Data
Covering Strategies
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Readings
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Markov, Z. Inductive methods for Machine Learning, Ch. 4.
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Programs
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Data
Propositional
lgg-based approaches
Relational
lgg-based approaches
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Readings
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Markov, Z. Inductive methods for Machine Learning, 8.9.
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Flach, P. Simply Logical: Intelligent reasoning by example, John Wiley
& Sons, 1994, Ch. 9.
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Programs
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Data
Building
specialization graph
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Readings
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Markov, Z. Inductive methods for Machine Learning, 8.9.
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Flach, P. Simply Logical: Intelligent reasoning by example, John Wiley
& Sons, 1994, Ch. 9.
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Programs
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Data
Using
information-based heuristics - FOIL
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Readings
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Markov, Z. Inductive methods for Machine Learning, Ch. 9.
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Quinlan, J.R. Learning logical definitions from relations, Machine Learning,
Vol. 5, 1990, 239-266.
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Quinlan, J.R, R.M. Cameron-Jones. FOIL: A Midterm report, in: Proceedings
of ECML-93, LNAI, Vol.667, Springer-Verlag, 1993, 3-20.
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Programs
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Data
Similarity
measures and Nearest Neighbour algorithm
Simple Bayes algorithm
Agglomerative
Clustering
Conceptual
Clustering (COBWEB)
Explanation-Based
Learning